Convolutional neural networks (CNNs) are widely used in computer vision and natural language processing. Field-programmable gate arrays (FPGAs) are popular accelerators for CNNs. However, if used in critical applications, the reliability of FPGA-based CNNs becomes a priority because FPGAs are prone to suffer soft errors. Traditional protection schemes, such as triple modular redundancy (TMR), introduce a large overhead, which is not acceptable in resource-limited platforms. This article proposes to use an ensemble of weak CNNs to build a robust classifier with low cost. To have a group of base CNNs with low complexity and balanced similarity and diversity, residual neural networks (ResNets) with different layers (20/32/44/56) are combined in the ensemble system to replace a single strong ResNet 110. In addition, a robust combiner is designed based on the reliability evaluation of a single ResNet. Single ResNets with different layers and different ensemble schemes are implemented on the FPGA accelerator based on Xilinx Zynq 7000 SoC. The reliability of the ensemble systems is evaluated based on a large-scale fault injection platform and compared with that of the TMR-protected ResNet 110 and ResNet 20. Experiment results show that the proposed ensembles could effectively improve the system reliability when suffering soft errors with an overhead much lower than TMR.

The Industrial Internet of Things (IIoT) is the essential component of Industry 4.0. Blockchain is a promising technology for secure data sharing and trustable cooperation between IIoT devices. However, the ever-growing transaction records make it difficult for the storage-limited IIoT devices to join the blockchain network. In this article, an adaptive compression scheme is proposed to decrease the storage volume on each node. In the scheme, the block body is compressed by representing the included transactions as their remainders stored in the distributed nodes. The original transaction could be recovered based on the Chinese remainder theorem. In particular, each node adapts its compression ratio according to its storage resource. The nodes storing more data have advantages in transaction recovery, introducing an incentive mechanism for efficient storage utilization. The theoretical analysis and simulation results show that the proposed scheme can achieve a high compression ratio with good service availability. The proposed scheme dramatically lowers the threshold for IIoT devices to join the blockchain network, which is important for the large-scale application of blockchain in Industry 4.0.

Blockchain has a broad development potential and application prospects. At present, huge storage volume on each node has become one of the primary bottlenecks that restrict the expansibility of blockchain. Optimization for storage mechanism has become a necessary way to accelerate the improvement of blockchain. There are a variety of solutions to relieve the storage burden by modifying the architecture of blockchain, but most of them weaken the decentralization property of the system, which is the core advantages of the blockchain. In this paper, an optimization scheme based on the redundant residual number system is proposed to dramatically reduce the storage volume of nodes in the blockchain system, and fault tolerance mechanism is designed based on the new storage scheme. The simulation experiments are performed to prove the effectiveness of the proposed mechanism.

This paper explores the role of blockchains in the development of Web 3.0 and the Metaverse. The success of these technologies is dependent on the utilization of decentralized systems like blockchains, which can store and validate data on identities and reputations and facilitate the exchange of virtual assets. Full nodes, which store the entire blockchain state and validate all transactions, are essential for the decentralization and reliability of the network. However, operating a full node is resource-intensive and can be expensive. To tackle this challenge, we propose an incentive mechanism that utilizes contract-theoretic methods to economically motivate users to support the sustainability and growth of the blockchain network. Our contract design addresses the problem of information asymmetry (e.g., users' revenue-generating capabilities and efforts) between users and the blockchain network. Additionally, we recommend providing diverse incentives based on the user's revenue-generating capabilities and efforts to assist the blockchain network in funding incentives. Our experimental results demonstrate that our proposed mechanism increases the blockchain network's utility by $48.48\%-54.52\%$ and reduces the users' cost by $38.46\%-62.5\%$ compared with the state-of-the-art implementations such as Celo, Vipnode, and Pocket Network.

Convolutional neural networks (CNN) have become essential for many scientific and industrial applications, such as image classification and pattern detection. Among the devices that can implement neural networks, SRAM based FPGAs are a popular option due to their excellent parallel computing capability and good flexibility. However, SRAMFPGAs are susceptible to radiation effects, which limits its application on safety critical applications. In this paper, the reliability of an accelerator based on the advanced Instruction-Set Architecture is evaluated based on hardware fault injection experiments. Each main module of the accelerator is evaluated separately, and the impact of parallelism and model features on the accelerator reliability is also examined. The experimental results reveal some important conclusions in terms of general hardware reliability and also of the particular model reliability. First, over 99% of SEUs on the computation modules will cause accuracy loss, and the reliability improves for higher parallelism. Second, a large portion of SEUs on the data mover and the instruction scheduler will cause system corruptions due to abnormal interactions with the ARM or other modules. Third, nonlinear activation and pooling layers are effective in reducing the effect of SEUs on computation modules, so models that use these layers tend to be more robust. The results provide a deep understanding of the impact of errors on CNNs implemented on ISA based FPGA accelerators (e.g., the Xilinx DPU).

In a public blockchain system applying Proof of Work (PoW), the participants need to compete with their computing resources for reward, which is challenging for resource-limited devices. Mobile blockchain is proposed to facilitate the application of blockchain for mobile service, in which the lightweight devices can participate mining by renting resources from the Cloud Computing Service Provider (CCSP), but CCSP usually does not have the information about the demand preference of users. In this paper, a contract model is adopted to address the cloud computing resource allocation and pricing problem in the mobile blockchain. In particular, an adverse selection contract solution is proposed to overcome the information asymmetry problem, and resource pooling is introduced to improve the stability of users rewards. Simulation results show that the information asymmetry problem is well overcome by adverse selection contract so that CCSP can obtain more utility than linear pricing contracts. Furthermore, the resource pooling could effectively improve the users and CCSPs utilities. When the size of the mining pool is large enough, it can achieve an improvement effect of more than 10 times. The effect of pool size and user type distribution on CCSPs utility is also studied.